在自然环境中使用机器视觉和深度学习检测工程结构的缺失螺栓
Zhenglin Yang1, Yadian Zhao2, Chao Xu1
1School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
一种新的机器视觉和深度学习方法可以准确地检测结构中缺失的螺栓. 这种自动化解决方案增强了螺栓连接的安全管理,即使在现实世界中,它也被证明是有效的.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确检测缺失的螺栓对于结构完整性和安全性至关重要.
- 现有的方法可能缺乏效率,准确性或适应各种条件的适应性.
研究的目的:
- 开发一种自动化,准确和高效的方法来检测工程结构中缺失的螺栓.
- 为了利用机器视觉和深度学习来增强螺栓检测.
主要方法:
- 为了模型培训,创建了一个全面的螺栓图像数据集.
- 对比深度学习模型 (YOLOv4,YOLOv5s,YOLOXs),选择YOLOv5s用于螺栓目标检测.
- 采用视角转换和对欧盟 (IoU) 交叉的方法实现了缺失螺栓检测方法.
主要成果:
- YOLOv5s模型在螺栓头 (0.93) 和螺母 (0.903) 中实现了高平均精度.
- 拟议的方法在各种条件下 (距离,角度,光线,分辨率) 准确识别了螺栓目标 (>80%的可靠性).
- 在一个真正的人行桥结构上成功检测到缺失的螺栓,甚至从1米远.
结论:
- 开发的方法为监控螺栓连接提供了一个低成本,高效和自动化的解决方案.
- 该方法在实际的工程应用中证明了可行性和有效性.
- 通过提供可靠的缺失螺栓检测来增强安全管理.
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